\Header{lvqinit}{Initialize a LVQ Codebook}
\keyword{classif}{lvqinit}
\begin{Description}\relax
Construct an initial codebook for LVQ methods.\end{Description}
\begin{Usage}
\begin{verbatim}
lvqinit(x, cl, size, prior, k = 5)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a matrix or data frame of training examples, \code{n} by \code{p}.

\item[\code{cl}] the classifications for the training examples. A vector or factor of
length \code{n}.

\item[\code{size}] the size of the codebook. Defaults to \code{min(round(0.4*ng*(ng-1 + p/2),0), n)}
where \code{ng} is the number of classes.

\item[\code{prior}] Probabilities to represent classes in the codebook. Default proportions in the
training set.

\item[\code{k}] k used for k-NN test of correct classification. Default is 5.

\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Selects \code{size} examples from the training set without replacement with
proportions proportional to the prior or the original proportions.\end{Details}
\begin{Value}
A codebook, represented as a list with components \code{x} and \code{cl} giving
the examples and classes.\end{Value}
\begin{References}\relax
Kohonen, T. (1990) The self-organizing map.
\emph{Proc. IEEE }
\bold{78}, 1464--1480.

Kohonen, T. (1995)
\emph{Self-Organizing Maps.}
Springer, Berlin.

Ripley, B. D. (1996)
\emph{Pattern Recognition and Neural Networks.} Cambridge.

Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition.  Springer.\end{References}
\begin{SeeAlso}\relax
\code{\Link{lvq1}}, \code{\Link{lvq2}}, \code{\Link{lvq3}}, \code{\Link{olvq1}}, \code{\Link{lvqtest}}\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data(iris3)
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
cd <- lvqinit(train, cl, 10)
lvqtest(cd, train)
cd1 <- olvq1(train, cl, cd)
lvqtest(cd1, train)
\end{ExampleCode}
\end{Examples}

